Unemployment Analysis¶

In [4]:
import numpy as np
import pandas as pd

import matplotlib.pyplot as plt
import seaborn as sns

import calendar

import datetime as dt

import plotly.io as pio
import plotly.express as px
import plotly.graph_objects as go
import plotly.figure_factory as ff
from IPython.display import HTML

C:\ProgramData\anaconda3\Lib\site-packages\paramiko\transport.py:219: CryptographyDeprecationWarning: Blowfish has been deprecated
  "class": algorithms.Blowfish,
In [14]:
df = pd.read_csv('data.csv')
#displaying the dataframe
df.head()
Out[14]:
Region Date Frequency Estimated Unemployment Rate (%) Estimated Employed Estimated Labour Participation Rate (%) Area
0 Andhra Pradesh 31-05-2019 Monthly 3.65 11999139.0 43.24 Rural
1 Andhra Pradesh 30-06-2019 Monthly 3.05 11755881.0 42.05 Rural
2 Andhra Pradesh 31-07-2019 Monthly 3.75 12086707.0 43.50 Rural
3 Andhra Pradesh 31-08-2019 Monthly 3.32 12285693.0 43.97 Rural
4 Andhra Pradesh 30-09-2019 Monthly 5.17 12256762.0 44.68 Rural
In [15]:
df.shape
Out[15]:
(768, 7)
In [4]:
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 768 entries, 0 to 767
Data columns (total 7 columns):
 #   Column                                    Non-Null Count  Dtype  
---  ------                                    --------------  -----  
 0   Region                                    740 non-null    object 
 1    Date                                     740 non-null    object 
 2    Frequency                                740 non-null    object 
 3    Estimated Unemployment Rate (%)          740 non-null    float64
 4    Estimated Employed                       740 non-null    float64
 5    Estimated Labour Participation Rate (%)  740 non-null    float64
 6   Area                                      740 non-null    object 
dtypes: float64(3), object(4)
memory usage: 42.1+ KB
In [5]:
df.isnull().sum()
Out[5]:
Region                                      28
 Date                                       28
 Frequency                                  28
 Estimated Unemployment Rate (%)            28
 Estimated Employed                         28
 Estimated Labour Participation Rate (%)    28
Area                                        28
dtype: int64
In [15]:
df=df.dropna()
In [14]:
df.isnull().sum()
Out[14]:
Region                                      0
 Date                                       0
 Frequency                                  0
 Estimated Unemployment Rate (%)            0
 Estimated Employed                         0
 Estimated Labour Participation Rate (%)    0
Area                                        0
dtype: int64
In [16]:
df.columns
Out[16]:
Index(['Region', ' Date', ' Frequency', ' Estimated Unemployment Rate (%)',
       ' Estimated Employed', ' Estimated Labour Participation Rate (%)',
       'Area'],
      dtype='object')
In [17]:
df.columns =['Region','Date','Frequency','Estimated Unemployment Rate (%)','Estimated Employed','Estimated Labour Participation Rate (%)','Area']
In [18]:
df.head(3)
Out[18]:
Region Date Frequency Estimated Unemployment Rate (%) Estimated Employed Estimated Labour Participation Rate (%) Area
0 Andhra Pradesh 31-05-2019 Monthly 3.65 11999139.0 43.24 Rural
1 Andhra Pradesh 30-06-2019 Monthly 3.05 11755881.0 42.05 Rural
2 Andhra Pradesh 31-07-2019 Monthly 3.75 12086707.0 43.50 Rural
In [19]:
df.describe()
Out[19]:
Estimated Unemployment Rate (%) Estimated Employed Estimated Labour Participation Rate (%)
count 740.000000 7.400000e+02 740.000000
mean 11.787946 7.204460e+06 42.630122
std 10.721298 8.087988e+06 8.111094
min 0.000000 4.942000e+04 13.330000
25% 4.657500 1.190404e+06 38.062500
50% 8.350000 4.744178e+06 41.160000
75% 15.887500 1.127549e+07 45.505000
max 76.740000 4.577751e+07 72.570000
In [21]:
round(df[['Estimated Unemployment Rate (%)', 'Estimated Employed', 'Estimated Labour Participation Rate (%)']].describe().T,2)
Out[21]:
count mean std min 25% 50% 75% max
Estimated Unemployment Rate (%) 740.0 11.79 10.72 0.00 4.66 8.35 15.89 76.74
Estimated Employed 740.0 7204460.03 8087988.43 49420.00 1190404.50 4744178.50 11275489.50 45777509.00
Estimated Labour Participation Rate (%) 740.0 42.63 8.11 13.33 38.06 41.16 45.50 72.57
In [22]:
#grouping by 'Region' and finding mean values for the numerical columns
areaStats = df.groupby(['Area'])[['Estimated Unemployment Rate (%)',
                                      'Estimated Employed',
                                      'Estimated Labour Participation Rate (%)']].mean().reset_index()

#rounding the values to 2 decimal points
round(areaStats,2)#grouping by 'Region' and finding mean values for the numerical columns
Out[22]:
Area Estimated Unemployment Rate (%) Estimated Employed Estimated Labour Participation Rate (%)
0 Rural 10.32 10192852.57 44.46
1 Urban 13.17 4388625.58 40.90
In [23]:
regionStats = df.groupby(['Region'])[['Estimated Unemployment Rate (%)',
                                      'Estimated Employed',
                                      'Estimated Labour Participation Rate (%)']].mean().reset_index()

#rounding the values to 2 decimal points
round(regionStats,2)#grouping by 'Region' and finding mean values for the numerical columns
Out[23]:
Region Estimated Unemployment Rate (%) Estimated Employed Estimated Labour Participation Rate (%)
0 Andhra Pradesh 7.48 8154093.18 39.38
1 Assam 6.43 5354772.15 44.87
2 Bihar 18.92 12366189.14 38.15
3 Chandigarh 15.99 316831.25 39.34
4 Chhattisgarh 9.24 4303498.57 42.81
5 Delhi 16.50 2627512.86 38.93
6 Goa 9.27 226308.33 39.25
7 Gujarat 6.66 11402012.79 46.10
8 Haryana 26.28 3557072.46 42.74
9 Himachal Pradesh 18.54 1059823.71 44.22
10 Jammu & Kashmir 16.19 1799931.67 41.03
11 Jharkhand 20.58 4469240.43 41.67
12 Karnataka 6.68 10667119.29 41.35
13 Kerala 10.12 4425899.50 34.87
14 Madhya Pradesh 7.41 11115484.32 38.82
15 Maharashtra 7.56 19990195.86 42.30
16 Meghalaya 4.80 689736.81 57.08
17 Odisha 5.66 6545746.96 38.93
18 Puducherry 10.22 212278.08 38.99
19 Punjab 12.03 4539362.00 41.14
20 Rajasthan 14.06 10041064.75 39.97
21 Sikkim 7.25 106880.71 46.07
22 Tamil Nadu 9.28 12269546.75 40.87
23 Telangana 7.74 7939662.75 53.00
24 Tripura 28.35 717002.64 61.82
25 Uttar Pradesh 12.55 28094832.18 39.43
26 Uttarakhand 6.58 1390228.11 33.78
27 West Bengal 8.12 17198538.00 45.42
In [25]:
fig, ax = plt.subplots(figsize=(10,6))
sns.heatmap(df.corr(), center=0, cmap='Blues')
ax.set_title('unemployment analysis')

C:\Users\User\AppData\Local\Temp\ipykernel_3484\3600111740.py:2: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.
  sns.heatmap(df.corr(), center=0, cmap='Blues')
Out[25]:
Text(0.5, 1.0, 'unemployment analysis')
In [31]:
fig, ax = plt.subplots(figsize=(10,6))
sns.heatmap(df.corr(), center=0, cmap='BuPu', annot=True)

C:\Users\User\AppData\Local\Temp\ipykernel_3484\611880948.py:2: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.
  sns.heatmap(df.corr(), center=0, cmap='BuPu', annot=True)
Out[31]:
<Axes: >
In [36]:
heatMap = df[['Estimated Unemployment Rate (%)', 'Estimated Employed', 
              'Estimated Labour Participation Rate (%)']]

#constructing on heatMap with correlation values
heatMap = heatMap.corr()

#plotting the figure
plt.figure(figsize=(23,8))
sns.heatmap(heatMap, annot=True,cmap='PiYG', fmt='.3f', linewidths=1)
plt.title('heatMap')
plt.show()
In [16]:
df = pd.read_csv('data.csv')
#displaying the dataframe
df.head()
Out[16]:
Region Date Frequency Estimated Unemployment Rate (%) Estimated Employed Estimated Labour Participation Rate (%) Area
0 Andhra Pradesh 31-05-2019 Monthly 3.65 11999139.0 43.24 Rural
1 Andhra Pradesh 30-06-2019 Monthly 3.05 11755881.0 42.05 Rural
2 Andhra Pradesh 31-07-2019 Monthly 3.75 12086707.0 43.50 Rural
3 Andhra Pradesh 31-08-2019 Monthly 3.32 12285693.0 43.97 Rural
4 Andhra Pradesh 30-09-2019 Monthly 5.17 12256762.0 44.68 Rural
In [25]:
u_emp=df[['Area',' Estimated Unemployment Rate (%)']].groupby('Area').sum().sort_values(by=' Estimated Unemployment Rate (%)', ascending =False)
u_emp
Out[25]:
Estimated Unemployment Rate (%)
Area
Urban 5016.48
Rural 3706.60
In [29]:
import plotly.express as pl
!pip install kaleido

Defaulting to user installation because normal site-packages is not writeable
Requirement already satisfied: kaleido in c:\users\user\appdata\roaming\python\python311\site-packages (0.2.1)
In [32]:
import plotly.express as px
df = pd.read_csv('data.csv')

fig = px.scatter(df, x="Area", y=' Estimated Unemployment Rate (%)', color=' Estimated Labour Participation Rate (%)',
                 title="Scatterplot")


fig.show(renderer='colab')
fig.show(renderer='notebook')
In [35]:
plt.figure(figsize=(12,10))
plt.title('Unemployment In India')
sns.histplot(x=' Estimated Unemployment Rate (%)', hue="Area", data=df)
plt.show()
In [37]:
plt.figure(figsize=(12,10))
plt.title('Unemployment In India State Wise')
sns.histplot(x=' Estimated Unemployment Rate (%)', hue="Region", data=df)
plt.show()

So this is how you can analyze the unemployment rate by using the Python programming language. Unemployment is measured by the unemployment rate which is the number of people who are unemployed as a percentage of the total labour force.